A multichannel temporally adaptive system for continuous cloud classification from satellite imagery

被引：10

作者：

Saitwal, K ^{[1
]}

Azimi-Sadjadi, MR

Reinke, D

机构：

[1] Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80523 USA

[2] Colorado State Univ, Cooperat Inst Res Atmosphere, Ft Collins, CO 80523 USA

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2003年 / 41卷 / 05期

关键词：

cloud classification; multispectral satellite imaging; probabilistic neural networks;

D O I：

10.1109/TGRS.2003.813550

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

A two-channel temporal updating system is presented, which accounts for feature changes in the visible and infrared satellite images. The system uses two probabilistic neural network classifiers and a context-based predictor to perform continuous cloud classification during the day and night. Test results for 27 h of continuous classification and updating are presented on a sequence of Geostationary Operational Environmental Satellite 8 images. Further test results of the system on two new sets of data with 1-2 weeks time difference are also presented that show the potential of this system as an operational continuous cloud classification system.

引用

页码：1098 / 1104

页数：7

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